surf.gls: Fits a Trend Surface by Generalized Least-squares

Description Usage Arguments Value References See Also Examples

View source: R/kr.R

Description

Fits a trend surface by generalized least-squares.

Usage

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surf.gls(np, covmod, x, y, z, nx = 1000, ...)

Arguments

np

degree of polynomial surface

covmod

function to evaluate covariance or correlation function

x

x coordinates or a data frame with columns x, y, z

y

y coordinates

z

z coordinates. Will supersede x$z

nx

Number of bins for table of the covariance. Increasing adds accuracy, and increases size of the object.

...

parameters for covmod

Value

list with components

beta

the coefficients

x
y
z

and others for internal use only.

References

Ripley, B. D. (1981) Spatial Statistics. Wiley.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

trmat, surf.ls, prmat, semat, expcov, gaucov, sphercov

Examples

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library(MASS)  # for eqscplot
data(topo, package="MASS")
topo.kr <- surf.gls(2, expcov, topo, d=0.7)
trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
contour(trsurf, add = TRUE)

prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
contour(prsurf, levels=seq(700, 925, 25))
sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
eqscplot(sesurf, type = "n")
contour(sesurf, levels = c(22, 25), add = TRUE)

spatial documentation built on May 29, 2017, 8 p.m.